International Journal of Artificial Intelligence, Data Science, and Machine Learning
Predictive Capacity Management in Teradata: AI-Driven Forecasting and Performance Optimization for Enterprise Data Warehouses
As more and more businesses rely on their Teradata for in-depth analytics, keeping an eye on the capacity of their data warehouses has become a big worry. Traditional static methods don't always function well with the changing nature of data growth, workload changes & changing their business needs. This article talks about a way to use AI to manage predictive capacity in Teradata. It focuses on their anticipatory forecasting & how they use their resources strategically. The suggested method uses ML models to look at how much storage, workload & query their performance have changed over time to make accurate guesses about how much capacity they will need in the future. It lets companies detect infrastructure problems before they happen, change how much storage & computing power they need on the go & keep the system running at its best without providing too much. The research uses anomaly detection to find many sudden spikes in workload & these idle resources. This ensures the system works well & expenses the least amount of money. Experimental results from enterprise-scale datasets demonstrate these significant enhancements in workload predictability, query response times & system throughput, yielding up to a 25% increase in their resource utilization and a 30% reduction in operating expenses compared to conventional threshold-based methodologies. The design makes sure that IT infrastructure is in line with actual business needs while also making sure that it can grow & be more reliable. This AI-powered strategy for managing these predictive capacity converts Teradata settings into these self-optimizing ecosystems. These ecosystems maintain their performance & efficiency high even as data expands and business analytics demands change
Developing Healthcare Knowledge Graphs through Graph Neural Networks
Healthcare Knowledge Graphs (KGs) have evolved into many powerful tools for organizing & connecting huge amounts of medical information into meaningful connections that may help with these clinical insights & decision-making. Nonetheless, the creation of effective knowledge graphs in their healthcare is challenging because of the diversity & complexity of medical data sources, including electronic health records, biological literature & genetic databases, each with unique formats & terminologies. This heterogeneity often leads to these inconsistencies, hindering the achievement of semantic interoperability & accurate data integration. This research explores the use of Graph Neural Networks (GNNs) for improving the intelligence as well as adaptability of medical data graphs in addressing those challenges. The proposed strategy leverages the representational capabilities of GNNs to increase learning from graph-structured information, therefore clarifying more complex relationships across many patients, diseases, treatments & biological entities. By finding subconscious patterns, identifying missing interactions & improving the knowledge graph's whole reasoning ability stronger, the method makes knowledge inference stronger. Experiments indicate that the use of GNNs substantially improves entity connections, connection estimations & diagnostic proposals in comparison to these traditional rule-based or statistical approaches. The results show how integrating graph-based instructional methods with their way of displaying health-related data might lead to evolving, interpretable & adaptable platforms that improve clinical choice-making, individualized treatment planning & medical research. This study demonstrates how enhanced GNN medical understanding graphs might boost the interconnection, data-driven nature & cognitive abilities of healthcare systems.
Secure Healthcare Data Sharing Ecosystems over Blockchain
In the present day's digital world, when privacy, accuracy & quick access to patient information directly affect medical outcomes, the safe & effective sharing of healthcare information has become a major issue. Even while these centralized data management systems are very common, they frequently run into many problems including data silos, poor interoperability & being vulnerable to these breaches or unauthorized access. These restrictions make it harder for healthcare providers, patients & researchers to work together, and they also hurt the confidence and integrity of their information. Blockchain technology offers a decentralized and unchangeable system that ensures their transparency, security, and smooth data flow amongst all parties involved in order to solve these problems. This research introduces a blockchain-based structure for the exchange of healthcare data, using distributed ledger technology to provide secure access control, immutable audit trails & interoperability across electronic health record (EHR) systems via the use of smart contracts. The framework uses a permissioned blockchain architecture to balance privacy as well as scalability. This lets healthcare companies keep ownership of their information while allowing controlled data sharing. A case study shows that the proposed technique greatly enhances data security, makes it easier for administrators to conduct their jobs & makes it easier to handle patient consent compared to other methods. Also, the blockchain-based technology makes it easier to track their information and build trust amongst stakeholders, which leads to a more open & cooperative healthcare environment. The results show that implementing blockchain to healthcare systems decreases data-associated risks and supports the development of patient-oriented, interoperable, and ethically sound health data infrastructures that could influence how healthcare is administered throughout the entire globe.
A Generative AI Framework for Data Pipeline Optimization and Analytical Performance Enhancement
The surprising increase in data-intensive workloads in enterprise and cloud environments has increased the call to find smart and agile methods of optimizing data pipelines. The dynamic nature of the contemporary modern analytical ecosystems makes traditional rule-based and fixed optimization strategies inadequate, leading to bottlenecks, latency as well as poor utilization of resources. The given paper suggests a Generative AI-based framework that analyzes, designs, and optimizes data pipelines autonomously to improve the overall analytical performance. The architecture uses large language models (LLM) and reinforcement learning to identify optimal pipeline designs, dynamically adapt resource deployment and optimize flow real-time tune ethics. The architecture also incorporates an automatic response loop of self-optimization constantly learning in response to workload trends, metadata and past performance indicators. Single-run benchmark performance of analytics workloads on experimental platforms show a substantial decrease of pipeline latency 42.7%, throughput 58.4% and compute cost 31.2% in comparison with automation benchmark methods. In addition, its machine engine record attained 92.3% percent accuracy in pipeline recommendation that accentuates its flexibility in different data processing settings. The suggested design provides the basis of self-engineered data architecture to support scalable cost-effective and self-optimizing analytic engines and grocery data valuable operations in the next generation of data-driven businesses
Dinesh Babu Govindarajulunaidu Sambath Narayanan
Generative AI–Enabled Intelligent Query Optimization for Large-Scale Data Analytics Platforms
The immense scale of data volume in the distributed cloud infrastructures has magnified the computational intensity of query processing in extensive analytics system like Data Lake, distributed SQL systems and real-time data warehouses. The rule-based and cost-based algorithm-based traditional query optimization methods are becoming inadequate with dynamics, heterogeneous sources of data, and unpredictable execution conditions. The study presents an Intelligent Query Optimization Framework that relies on an AI to automatically rewrite the queries, optimize the plans, and execute them dynamically using the Generative Artificial Intelligence (Gen-AI). The proposed system term employs reinforcement learning (RL), natural language processing (NLP), and deep neural architecture to produce optimized query schemes, approximate the best implementation plans, decrease latency and enhance the throughput in gigacursing assemblies. Workloads, execution history of queries and run-time performance metrics on the distributed systems like Apache Spark, Presto, and Snowflake are all used to train the system. The research contributions consist of: (i) a synthesis mechanism to generate query plans through the use of transformer-based models, (ii) modeling to predict the costs of the workload, (iii) a multi-objective optimization scheme that minimizes the execution time, resource consumption, and cost of data transfer, and (iv) a hybrid architecture in which a batch and a streaming analytics is executed. Extensive experimental findings using the TPC-H benchmark show how it improves performance by 41%-percentage change in query latency, 32-percentage changes in throughput, and 27-percentage changes in memory usage as compared to state-of-the-art optimizers. The presented framework can be scaled, flexible and proficient in the changing data environment, which is a remarkable breakthrough in intelligent data analytics
Using Salesforce CRM and Deep Learning (CNN) Techniques to Improve Patient Journey Mapping and Engagement in Small and Medium Healthcare Organizations
Healthcare firms are becoming more and more reliant on predictive analytics and adaptive technology solutions to promote this engagement with the patient to make operations more efficient now and in the future. This paper investigates the process of illustrating merging of Convolutional Neural Network (CNN) as a class of deep neural networks in Salesforce CRM today by fine tuning the implementation of CNN to work on large datasets to deliver personalization and lifecycle governance of the Patient journey with the basic objective to compliment small and medium healthcare organizations across India. Using a mixed-methods design, quantitative data from 450 healthcare facilities are combined with CNN-based predictive modeling to examine points of entry into care, admission forecasts, and predictions of lifecycle transitions. The hypothesis states that the integration of CNN with the CRM systems can lead to significant improvements in patient satisfaction scores, reduce administrative costs and enhanced care coordination as compared to traditional CRM implementations. The result is a 34% increase in patient engagement metrics, 28% decrease in no-shows for appointments, and 41% increase in treatment adherence rates. Statistical analysis indicates significant association of predictive accuracy and optimization of patient outcomes. The CNN model could indicate transitions within patients lifecycle with 92% accuracy, enabling preventive actions. Then it discuss the learning-enabled CRM systems as transformative in resource-limited healthcare environments, focusing on scalability and ethics. Low-cost yet robust adaptive technologies are needed to ensure sustainable healthcare delivery during the pandemic and beyond, as is the need for policy frameworks that support digital transformation to meet healthcare demand in emerging healthcare markets
Quantum Algorithms for Optimization and Machine Learning
Quantum computing offers a fundamentally new paradigm for solving complex optimisation and learning tasks by exploiting superposition, entanglement, and other quantum phenomena. Over the past decade, research has increasingly focused on quantum-enhanced approaches to optimisation and machine learning (ML), showing the potential to outperform classical methods in specific settings (Zaman, 2023; Peral-García, 2024). This paper presents a comprehensive review of quantum algorithms for optimisation (e.g., the Quantum Approximate Optimization Algorithm and quantum annealing) and quantum machine learning frameworks (such as quantum support vector machines and quantum neural networks), analysing their theoretical underpinnings, implementation status, and applicability to real-world tasks. Key challenges including hardware noise, limited qubit counts, and algorithmic scalability are examined (Chen, 2024). We also explore hybrid quantum-classical architectures as a near-term route to quantum advantage and propose future research directions aimed at bridging the current gap between quantum algorithm theory and large-scale deployment. Through this synthesis, we aim to provide both academic and practitioner audiences with a clear roadmap for leveraging quantum algorithms in optimisation and ML workflows
Quantum Approximate Optimization Algorithm (QAOA)
Dynamic Workload Placement across Multi-Clouds: AI-Driven Cost Optimization without Downtime
Dynamic workload placement across multiple cloud providers enables organizations to optimize cost, performance, and reliability. This paper proposes an AI-driven framework that continuously evaluates pricing, latency, and resource availability across providers such as AWS, Azure, and Google Cloud Platform (GCP) to determine the most cost-effective environment for each workload. The system dynamically migrates workloads between providers without downtime by using predictive analytics, live-migration containers, and federated orchestration policies. This conceptual study outlines the architecture, algorithms, and benefits of intelligent workload placement while highlighting challenges in interoperability, cost modeling, and compliance
Securing Broadband Subscriptions on SaaS with OAuth2.0 in REST API
The rapid evolution of cloud-native technologies such as Software as a Service (SaaS) has transformed the landscape for Broadband Service Providers (BSPs), enabling the delivery of subscriber services with high scalability and efficiency. Initially, the adoption of such platforms was driven with a cause for simplifying workflows and increase usability for BSP operational teams. Over time, however, both BSPs and their associated Operations Support Systems (OSS) and Business Support Systems (BSS) have become increasingly reliant on these cloud-based solutions. This has resulted in the accumulation and management of large quantities of sensitive data, including personally identifiable information (PII), as well as critical internet and voice service records for individual subscribers. Basic authentication method typically involved the exchange of simple username and password which were stored in databases. Basic auth. proved inadequate in securing sensitive information against evolving security threats. As regulatory requirements and industry standards have grown more stringent, BSPs have been compelled to transition to more robust authentication frameworks. OAuth 2.0 has emerged as the industry standard for secure data transport, offering enhanced protection through token-based authentication and granular access controls
Use of Federated Learning in Iiot (Industrial Internet of Things) Security
Federated learning (FL) is a distributed machine learning paradigm that enables devices or edge nodes to collaboratively train a global model without sharing raw data. This enhances privacy and reduces communication overhead. As Industrial Internet of Things (IloT) systems expand across manufacturing, energy, and logistics, security has become critical. Centralized learning approaches struggle with privacy risks, latency, and single points of failure. This article explores the application of FL to strengthen cyber-physical system (CPS) security in IIoT. We present a framework for threat detection, anomaly recognition, and intrusion prevention that utilizes lightweight models, secure aggregation, and differential privacy for resource-constrained industrial devices. Experiments on a simulated smart-factory testbed show that our approach achieves accuracy comparable to centralized methods while maintaining data locality and resilience. We also analyze trade-offs, challenges, and future directions for deploying FL in real-global lloT environments
The Role of Artificial Intelligence in Predicting Credit Risk
Credit risk forecasting remains one of the key critical issues in financial risk management with the potential to impact lending rates, portfolio construction, capital allocation, and regulatory requirements. Conventional statistical techniques like logistic regression, discriminant analysis, and scorecard models have formed the backbone of credit assessment for many decades, but tend to be restricted by linear assumptions, limited learning ability, and difficulties in capturing non-linear behavioral characteristics (encapsulated in borrower data). There have been recent developments in the field of Artificial Intelligence (AI), in particular, machine learning (ML) and deep learning (DL), which have completely changed the paradigm for credit risk modelling. Via these methods, higher predictive performance can be achieved with the possibility of adapting to heterogeneous and high-dimensional data as well as integrating alternative and behavioral information, which classic modelling frameworks are unable to fully utilise. This paper provides an in-depth discussion on the potential of AI for credit risk estimation as well as its methodological upgrading, operational implementation regulatory frameworks that could support financial institutions applying AI-based scoring systems. Based on a review of the literature, ensemble learning techniques, and particularly gradient boosting techniques like XGBoost and LightGBM, have shown robust and discriminative performance against classical statistical models across studies, especially with noisy or missing data. Highly Nonlinear: Deep learning methods, with a surge in popularity, have shown inconsistent performances on structured credit data; they have been demonstrated to be effective only when including high-frequency non-linear features or complex behaviors, as well as unstructured information such as transaction sequences or text. The approach combines best practices from academia and industry for research to deployment, including data pre-processing, feature engineering, fairness checking, cost-sensitive learning approaches, model explainability methods, and governance controls. XAIthrough methods like SHAP and LIMEbecomes instrumental in enabling regulatory approval, model transparency, and stakeholder confidence. Furthermore, consideration of fairness has become essential given the evidence of negative consequences of unintended bias propagation in ML systems. The paper demonstrates how AI models can be calibrated, interpreted, and monitored to comply with legal, ethical, or operational constraints while preserving predictive performance. Experimental results show on a real-world public lending dataset that AI models outperform traditional credit scoring baselines, in terms of ROC-AUC, Precision-Recall AUC, and cost-weighted loss. Gradient-boosted decision trees provide the most balanced compromise of all between predictive performance, computation time and explainability. Only through access to more sophisticated temporal or high-dimensional behavioral features do our neural network models even perform on par with others in the literature, as recently reported. Explainability studies also show that borrower payment history, utilization patterns, and delinquency indicators are the most important features in all models tested. Fairness diagnostics reveal subgroup differences that thresholds/pre-processing/fair-optimization need to account for. The results as a whole reinforce that AI, when operationalized under stringent methodological controls, an interpretability framework, and fairness safeguards, can offer dramatic improvements in the predictive power and business utility of credit risk assessment systems. Finally, the paper provides practical guidelines for using AI-based credit scoring in financial services and identifies a number of promising research directions, such as causality modeling, privacy-preserving computation, and standardized fairness benchmarks. This holistic study yields a publication-ready, academically sound contribution for financial AI research that is in line with the future industry tendencies as well as latter supervisory and ethical demands on credit risk modelling
Cost-sensitive learning; Model calibration
Temporal Graph Neural Networks for Real-Time Fraud Detection in Cross-Border Transactions
Cross-border financial transactions are increasingly vulnerable to sophisticated and coordinated fraud attacks that evolve rapidly over time. Traditional fraud detection systems struggle to capture temporal dependencies and relational patterns inherent in real-time transaction networks. In this paper, we propose a novel approach using Temporal Graph Neural Networks (TGNNs) to detect fraudulent behavior in streaming, multi-national payment data. By modeling dynamic transaction graphs with time-aware message passing and temporal node embeddings, our framework captures both short-term anomalies and long-range dependencies. We design a low-latency inference pipeline capable of real-time deployment in financial networks. Experimental evaluation on synthetic and real-world cross-border transaction datasets demonstrates that our TGNN-based model significantly outperforms traditional machine learning and static GNN baselines, achieving up to 18% higher AUC and reducing false positives by 25%. The results highlight the potential of temporal graph learning to enhance security, compliance, and trust in global financial systems
The Effectiveness of Social Media in Customer Risk Awareness
Social media has taken centre stage as one of the most widespread avenues for information transfer and is fundamentally altering client attitude towards risk. Social media platforms are different than previous communication venues, from the print and broadcast press to institutional alerts, in that they are fast, interactive, algorithmically sensitive to each user’s profile, and with a capacity for distributed production of messages. These characteristics represent unprecedented opportunities for organizations, regulators, and public agencies to communicate risk information that is timely and actionable. At the same time, however, they add layers of volatility and uncertainty that make it harder for customers to understand real risks and allow misinformation, emotional hype, and unverified peer influence to warp that perception of risk. Given this context, the current study provides a comprehensive mixed-methods examination of how well social media increases customers’ awareness of risks in various contexts, including financial, public-health, and safety. The paper contends that effectiveness should be a multidimensional assessment-considering not just the increase in consumers' awareness of risk but also whether their awareness is accurate, translated into protective moves, and whether this communication setting attenuated distortion/exaggeration. Based on a 12-month content compendium and analyses of social media posts, a controlled experimental survey (N≈1200), and secondary enforcement actions and platform moderation policies, the study offers empirical evidence about what facilitates or hinders effective risk communication. The content analysis shows that official agencies generate very credible posts with a verifiable list of sources; however, these are dwarfed by the number of peer and influencer-owned pieces, which carry less authoritative footing and often feature sensational emotional framing. One fifth of the total sampled posts included deceptive markers illustrating the point that only in conjunction with network and social factors do contemporary platforms exhibit structural vulnerabilities. The experimental results show that source credibility is the main driver of successful risk awareness; that posts by official regulators are trusted, believed to be true, and overall intend to take action more against securing protection than influencers or peer users. It also depends on how the message is framed. Emotional posts may heighten perceived gravity and urgency, but do not produce meaningful enhancement in accurate comprehension or responsible behavioral intentions unless supplemented by specific actionable instructions. Additionally, credibility is demonstrated to serve as a partial mediator of the relationship, explaining why official sources are more effective than influencer content in motivating protective behavior even when controlling for message content. The policy and moderation analysis extends the empirical findings by providing insight into regulatory actions in particular, financial areas that aimed at restraining deceptive promotions and low exposure to risky communication. The analysis does, however, reveal substantial variation in the quality of enforcement across platforms and domains, so coordination efforts are still necessary. In the end, according to the study, social media is an effective yet imperfect means of raising customer risk awareness. As a tool to be used in good faith, by real voices with tight messaging and clear calls-to-action, AI can make it easier for consumers to protect themselves and mitigate risk. Alternatively, when free to be dominated by creative content, it can lead people to think and act in terms of better or worse ways of being; infected by more emotive or misleading data sets, it risks injecting error into our view of affairs, generating panic and bad judgment. This paper presents a robust methodological approach and, beyond that, provides policymakers, practitioners, and scholars with the critical evidence-based and actionable knowledge necessary to develop risk-communication strategies in digitally mediated contexts
AI-Enhanced Event Tracking: A Collaborative Full-Stack Model for Tag Intelligence and Real-Time Data Validation
The current digital analytics systems strongly depend on precise tracking of events in order to measure user interactions, drive personalization pipelines, and make business decisions that are revenue critical. Nevertheless, current tagging systems still have the issue of fragmented instrumentation, the inconsistency of schema compliance, such as data drift and this interestingly missed event without any exception-in-curring serious costs in data quality and the performance of end model. To overcome them, this paper suggests an AI-Enhanced Event Tracking Framework which operates on a collaborative full-stack design to bind the front-end tag instrumentation, middleware validation logic and backend real-time anomaly detection into one intelligence-driven system. The proposed architecture takes advantage of three layers of automation: (i) an intelligent front-end instrumentation engine based on dynamically validating the DOM context, identification of broken or hand-fired tags and enrichment events with contextual metadata; (ii) policy-based middle layer based on the enforcement of event contracts, business rules, and schema constraints with the use of deterministic rules augmented by ML-based decision scoring; and (iii) a back-end AI validation engine based on prediction of event correctness by means of supervised classifier, sequence level anomaly detection by unsupervised drift model and the empirical analysis of production scale data sets substantiates that the system leads to the enhancement of data reliability end to end considerably. Findings have indicated 41-63 percent decrease in the missing or malformed events, 28 percent decrease in the duration of validation as well as 35-52 percent enhancement in the accuracy of detection of anomalies relative to the base rule-based validators. Moreover, the architecture allows high-throughput streaming operations to be maintained with real-time inference of more than 50k events per second at minimal overhead. Together, these contributions create a scalable and self-governing platform of tag intelligence, real-time data validation, and allow organizations to have constantly reliable pipelines of analytics and solid observability through the digital ecosystem
Governance-of-Things (GoT): A Next-Generation Framework for Ethical, Intelligent, and Autonomous Web Data Acquisition
The unstoppably increasing number of the Internet of Things (IoT), autonomous agents, and massive distributed web ecosystems have made data acquisition a complicated, risk-prone, and a very sensitive process. Regulation Web data collection is a fixed pipeline that is strictly regulated by established rules and legal limits, and reactive policy audits to operate in traditional forms of governance. Nevertheless, the contemporary digital ecosystem requires a decentralized system of governance that could identify unpredictable streams of data, the shifting web framework, loosely distributed computing individuals, and shifting conditions of regulation. This paper will present Governance-of-Things (GoT), an emerging conceptual and architectural design that will address these issues and show how to smoothly integrate ethical intelligence, regulatory and laws compliance, semantic awareness, and integrity assurance within autonomous systems of web data acquisition. GoT suggests a view where governance follows a first-class computation i.e. embedded, adaptive, intelligent and context-aware. In contrast to traditional approaches of governing IoT, GoT regards any acquisition agent as ethics-regulated, compliance-aware, and self-regulating. Agents do not simply pull information, they negotiate access rights, authenticate provenance, reason about risk, and implement multi-jurisdictional policies all by themselves. The framework combines dynamic enforcement of policies, federated governance, semantic classification pipelines, AI-enhanced agent frameworks built on Java and distributed analytics to create an ecosystem, producing an automated acquisition that is compatible with responsible, transparent, and audit-friendly behaviours. Fairness, legality, transparency, explainability and accountability are the principles of ethical autonomy which are expounded in the paper. GoT has the aspect of federated ethical rule orchestration where the governance layers among organizations in various stakeholders share without necessarily providing the raw information. The system incorporates automation using structural integrity that guarantees cryptographic validation and review trails that are not tampered with. The given adaptive monitoring model promotes the constant policy updating, data flows redirection and the detection of threats. Furthermore, GoT involves semantic intelligence so that data classification, contextual labeling, entity recognition, and domain mapping take place before storing or processing data- therein avoiding compliance violation at its early phases. GoT architecturally has a multi-layer stack that is organized and includes Perception Layer, Autonomous Agent Layer, Governance Core, Distributed Analytics Layer, and Compliance Ledger Layer. The primitives of computational governance are embedded in each layer, making it highly modular and allowing run-time updates of rules and cooperating across agents. Java frameworks boosted with AI facilitate interoperability with legacy enterprise systems and with current base systems. Using experimental simulation, it was found that GoT enhances compliance accuracy, governance throughput, policy adaptation latency and decision explainability on varying scenarios of acquisitions. This article is in the pre-2021 academic style, has extensive literature review, methodological description, architectural schematics, theoretical framework, and profound results discussion. It ends by establishing GoT as an innovative paradigm which is able to influence the future of web data governance, autonomous systems, and distributed analytics
AI-Enabled Policy-Driven Web Governance: A Full-Stack Java Framework for Privacy-Preserving Digital Ecosystems
The digital ecosystems have experienced a paradigm shift as there is a growing level of integration of Artificial Intelligence (AI), distributed computing apparatus, and robotic policy enforcement strategies. Governance structures are faced with the difficult task of negotiating the vagaries of privacy laws and decentralized data processing and the use of algorithmic decision-making with the migration of data-intensive applications to web-based environments. The increasing regulatory environment, such as GDPR, CCPA, and industry-specific data protection requirements, have significant forces on the requirement to have strong policy-driven governance infrastructures that entrench privacy and security at every layer of the web application stack. Although cloud platforms and microservice architectures have been developed, modern governance solutions have weaknesses in terms of scalability, being context-aware and dynamically adapting to changes in policy constraints. The research paper presents the AI-Enabled Policy-Driven Web Governance Framework that has been developed on the Full-Stack Java ecosystem which involves spring boot, Jakarta EE, containerized deployment platforms and intelligent agents which are rule-based. The framework incorporates machine learning-related policy interpretation, semantic arguments engines, as well as automated monitoring applications that regulate user interactions, data activities, service coordination, and cross-layer correspondence. AI agents will adapt legal and organizational privacy requirements into dynamic policies that are explicitly and dynamically implemented in real-time at the front-end, API, middleware, and database tiers. These challenges in digital governance that are solved are minimization of data, contextual privacy, verification of compliance, detection of anomalies, and fine-grained access control. The given architecture proposes a Multi-Layer Governance Orchestration Model (MGOM) that divides the governance issues into policy ingestion, AI interpretation, runtime enforcement, auditability, and compliance reporting. The framework also includes three levels of privacy shield with a static code analysis, user behavior analytics (UBA), and encrypted data pipelines. Through an extensive assessment analysis, it is evident that the framework has the ability to be highly precise in automated policy enforcement, decreases the latency of governance and enhances consistency of compliance over the traditional rule-based systems. The findings of the experiments point out that AI-enabled governance engine helps to improve the accuracy of policy compliance by 27.8 percent, minimize privacy invasions by 42.1 percent, and decrease administrative workload by 34.6 percent. A combination of a supervised learning, the natural language processing (NLP) and the symbolic rule mining allow the system to be autonomously adapted to new regulatory conditions without being reconfigured by human operators. Security benchmarks also indicate resiliency to partial attack vectors, such as inference attacks, unauthorized data elevation, and access patterns analysis. The paper will add value to the digital governance field by offering a holistic, scalable, and future-proof implementation that can assist with current web environments of many services including medicine, finance, online commerce, and smarter cities. The framework ensures the creation of a novel model of transparent, compliant, and privacy-conscious digital ecosystems by entrenching AI at the core of policy interpretation and enforcement. The publication contributes to the discussion of intelligent governance systems and offers a reference design to the developers, policymakers, and researchers, who seek to develop trustful and ethically aligned digital spaces
What’s the True ROI of Copay Programs? A Transparent Framework for Evaluation
The copay assistance benefit programs lower out of pocket (OOP) patient expenses, and may enhance therapy initiation and compliance, but their actual ROI is commonly lost in confounding policy processes and accounting methods. The present paper gives a clear audit-compliant structure of ROI as a risk-adjusted incremental contribution margin assigned to copay exposure excluding gross-to-net factors (rebates, chargebacks, fees), operating costs, and compliance risk. The framework breaks down impact into four causal levers initiation, adherence/persistence, channel steering and price elasticity and relates them to what can be observed (incremental new starts, days on therapy, refill cadence, payer mix). It will be identified using quasi-experimental designs (difference-in-differences, regression discontinuity, instrumental variables, and synthetic controls) and survival analysis to ascribe adherence benefits to lifetime value. An explicit guideline on policies such as accumulator/maximizer program, step edits and specialty carve-outs is to keep benefits overstated. Governance guardrails are pre-registered analysis plans, data lineage, and sensitive panels are included, making results comparable across brands and time. Applied scenarios reveal that copay programs are value-generating under high elasticity and abandonment risk and high accumulator exposure and low cannibalization and high policy frictions, but value-destroying under dominance of cannibalization and high policy frictions, respectively. What it delivers is an effective toolkit that standardizes ROI equations, decision dashboards, and scenario testing that will allow financial stewardship to meet patient access and allow credible reporting to manufacturers, payers, providers and researchers
Generative AI for Network Attack Simulation
Generative Artificial Intelligence (AI) holds remarkable possibilities in a host of applications, such as cybersecurity and various other fields. This paper presents and examines the application of Generative AI for the Simulation of Network attacks. It highlights its key characteristics, areas of cyber security and data science, challenges, and opportunities in addressing the problem of cybercrimes. The research presented in this paper explores the use of Generative AI techniques, such as GANs, to simulate complex cyberattacks with rigor and reliability, thereby spurring the authentication of sophisticated phishing algorithms and bolstering the fortification of sophisticated cybersecurity systems. This study aims to evaluate the feasibility of using AI to conduct simulated network attacks compared to traditional methods. It is important to note that this research was conducted authentically and was done entirely through the care, process, and support of the designated University. In addition, the organizations can foresee the defects within their network and respond much quickly by simulating realistic attack scenarios through patented technologies (such as Generative AI). Subsequently, it proposes that research in this area is also essential to continually assist in innovating responsibly and to address potential risks associated with increasing autonomy of AI systems, e.g., risks of AI systems having a security exploit with button content set as skip and the attribute set as. Additionally, adopting machine learning approaches for Network Attack Simulation would offer more reliable insights into predicting adversary movements, thereby enabling the design of security measures that can potentially prevent critical states in linear time. At the same time, a future emerged where we barely understand the generative adversarial network. In that future, we will need to identify them by leveraging our knowledge of Generative AI technologies and capabilities
Generative Artificial Intelligence (Generative AI)
Federated Learning Infrastructure for Privacy-Preserving Personalized Shopping in E-Commerce
The modern e-commerce landscape is defined by a fundamental tension between the strategic imperative for real-time, personalized user experiences and the dual constraints of stringent privacy regulations and high-latency computation. While personalized recommendations are critical for navigating information overload and driving engagement, traditional centralized architectures pose significant data privacy risks. Federated Learning (FL) has emerged as a foundational, privacy-preserving paradigm by training models on decentralized user data. However, standard FL implementations suffer from a severe communication bottleneck, introducing latency that is prohibitive for time-sensitive applications like product recommendations at checkout. This paper addresses this performance gap by introducing a new class of tooling exemplified by the Low-Latency Federated Learning (LoLaFL) framework. It details the architectural shift from conventional backpropagation-based FL to a novel forward-only, white-box framework. This approach dramatically reduces communication overhead and computational complexity, enabling the low-latency inference required to deliver secure, private, and instantaneous personalized recommendations at the most critical point of the customer journey
Exponential Pine Cone Optimization Enabled Quantum-Inspired Convolutional Neural Networks for Secure Network Slicing in 5G Network
Network Slicing is significant in facilitating a multiple of Fifth-generation (5G) applications, use cases, and services. It provides end-to-end isolation among the slices for customizing each slice in terms of service demands. The increasing demands of 5G networks and advanced features to meet complex business needs have rendered conventional approaches insufficient. Therefore, an efficient approach is required to mitigate the issues in the slicing process. As a result, Exponential Pine Cone Optimization enabled Quantum-Inspired Convolutional Neural Network (EPCO_QuCNet) is introduced for secure network slicing in 5G network. The system model of 5G network is carried out. When network slicing receives requests from a User Equipment (UE), various network parameters, like speed, packet loss rate, jitter, and packet delay budget—are gathered from multiple devices. Hence, a secure slicing of network is classified by Quantum-Inspired Convolutional Neural Networks (QuCNet), which is trained by Exponential Pine Cone Optimization (EPCO) algorithm. Furthermore, the services provided in 5G is accessed by the internet service provider using Virtual Network Function (VNF). EPCO_QuCNet has achieved better outcomes for the evaluation metrics, like acceptance rate, execution time and resource efficiency of 0.927, 0.167 sec and 0.911
Quantum-Inspired Convolutional Neural Networks
Architecting, Grounding and Governing – Telecom Supply Chain GenAI Agents with SAP AI Foundation
Modern supply chains demand unprecedented agility, resilience, and intelligence to navigate the volatility of rapid technology cycles, complex network rollouts, and critical infrastructure dependencies. Generative AI (GenAI) agents offer transformative potential, automating complex decision-making and optimizing processes. However, realizing this potential requires robust frameworks for their architecture, grounding in reliable data, and responsible governance. This white paper details a comprehensive approach to architecting, grounding, and governing Telecom Supply chain GenAI agents leveraging SAP AI Foundation on the SAP Business Technology Platform (BTP). It explores methodologies for building context-aware, reliable, and ethically aligned agents using components like SAP AI Core, Generative AI Hub, SAP HANA Cloud Vector Engine, and SAP Knowledge Graph. The paper outlines the benefits, including enhanced efficiency, enhanced network resilience, improved field service efficiency, optimized inventory, improved decision-making, and increased resilience, alongside implementation considerations and use cases, targeting supply chain professionals, IT/AI architects, and business leaders seeking to harness GenAI responsibly within the SAP ecosystem and architecting Gen AI agents
Privacy-Preserving Federated Learning Frameworks for Telematics Data in Auto Insurance Analytics
In particular, the use of telematics data in auto insurance has dramatically changed risk underwriting and pricing but raises serious privacy issues for drivers sensitive information. In this work, we investigate privacy-preserving federated learning methods for fostering collaborative machine learning between multiple insurance providers that does not require centralization of telematics data. Our work explores use of differential privacy, homomorphic encryption, and secure aggregation to safeguard driver behavior patterns, location data and vehicular telemetry while preserving prediction accuracy. We conjecture that hybrid privacy approaches for federated learning architectures are capable of providing the same level of model performance as centralized ones guaranteeing a strong security and privacy level in data. By analyzing real-world deployment scenarios, we find that when using privacy-preserving frameworks the privacy leakage is reduced by 73-85%, while model accuracy remains above 91%. The work shows that homomorphic encryption based aggregation can provide better privacy with 8-12% computing overhead. These findings show that federated learning frameworks can be potential solutions to insurance telematics analytics, which allows for risk modeling in a collaborative way under data protection regulation and competitive intelligence through privacy-preserving collaboration mechanism
Conversational AI in Salesforce: A Study of Einstein Bots and Natural Language Understanding
Chatbot AI is turning into an essential part of the contemporary customer management beliefs (CRM). In order to automate customer support, lead generation, and the process of workflow management, Salesforce, a market leader in CRM, has introduced AI-driven virtual dimininiske povrh2718 noticeable bots, so-called Einstein Bots. The present paper assesses the success of the Salesforce Einstein Bots with reference to Natural Language Understanding (NLU), end-user satisfaction and real-life issues of implementation. We discuss the issues of conversational models in Salesforce regarding multilingual input, intents identification, extraction of entities and fallback cases. The paper is based on a review of literature, architectural survey and simulation of enterprise. In our findings, it has been indicated that Einstein Bots can work in structured settings but they have a weakness on contextual awareness and interpretation of sentiments. The paper provides suggestions on how to improve NLU pipelines and describes the best practice on optimising user experience. Our strategic recommendations to strike a balance between the effectiveness of automation and user activity in chatbot implementations in Salesforce are the conclusion
Hybrid AI-Based Threat Prediction and Mitigation Framework for Secure Cloud Storage: A Rigorous Critical Review of Methodological Challenges and Future Research Directions
In the fast-changing world of cloud computing, maintaining the confidentiality, integrity, and availability of data is still a major challenge. The research suggests a framework of Hybrid Artificial Intelligence (AI)-Based Threat Prediction and Mitigation which is intended to improve the security of cloud storage by using integrated machine learning (ML) and deep learning (DL) techniques. The study uses a statistically validated dataset (N = 50) that contains specifications like AI model accuracy, false positive rate, p-value, cloud storage security score, mitigation efficiency, and detection latency. Through descriptive and inferential analyses, it is revealed that the model obtains 91.2% overall accuracy, 2.44% false positive rate while at the same time, the average mitigation efficiency is 84.5% plus the latency of detection is 104 ms. The p-value = 0.00259 < 0.005 indicates that the improvement in the model’s performance is statistically significant. The findings imply that hybrid AI techniques can effectively reduce the number of false alerts and improve the speed of real-time response in the situation of distributed cloud infrastructures. The proposed framework is shown to be wide-ranging, flexible, and dependable in terms of prediction, thereby playing a part in making the cloud ecosystems secure and self-sufficient
AI-Enhanced ETL Framework for Improving Data Quality in Clinical Decision Support Systems
Clinical Decision Support System (CDSS) are inalienable parts of modern healthcare in that they offer an informational support to clinicians regarding diagnosis, treatment planning, and management of patients. Their credibility however depends on the quality of the information obtained on the Electronic Health Records (EHRs) and other heterogeneous sources. Lacking, disjunctive, and semantically varied data remain as major hindrances to fruitful decision-making. This paper suggests an AI-powered Extract, Transform, Load (ETL) system that relies on machine learning, natural language processing, and ontology-based reasoning systems to improve healthcare data quality automatically. The framework uses anomaly detection using autoencoders, entity detection using BioBERT, and semantic harmonisation using HL7-FHIr and SNOMED-CT ontologies. A feedback loop that is reinforcement-learning further optimises changes with time. The experimental analysis of publicly available MIMIC-III and PhysioNet data indicates a 26% high data completeness, a 17% higher rate in consistency, and a 10% higher rate of CDSS diagnostic stability compared to the traditional ETL tasks. These results show that AI-based ETL technology can significantly improve data quality, interoperability, as well as generating clinical insights, thus leading to a platform that enables more credible and scalable CDSS designs
Big Data Pipeline Optimisation for Electronic Health Records (EHR)
The vast utilisation of Electronic Health Records (EHRs) has led to a revolution in healthcare organisations worldwide. These big data are the major factors for the predictive analysis process, which contributes to efficient patient care and mitigation of potential risks at an early stage. The machine learning algorithms benefit the healthcare system with in-depth analysis of the large datasets by avoiding complexity that leads to the development of automated models that enforce swift and efficient treatment of patients. Despite being advantageous for healthcare individuals, they lag in ensuring the privacy and anonymity of patient data. Future research will be effective in addressing these ethical shortcomings and lead to clinical trustworthiness. Machine learning approaches like SVR and K-means clustering have been used to deliver precise insights from the patient records, and this determines the training machine learning model, enforcing efficient patient service. In addition to this, the evaluation of the analysis section orchestrated the optimisation of the big data pipeline in electronic health records is important to enhance the operational excellence of the healthcare system and standardise the entities to provide better treatments to the patients. In the context of future work, the presentation of new approaches of artificial intelligence and deep learning could be effective in optimising the functionalities and features of the electronic health record further with immaculate accuracy
Data Quality Assessment and Cleaning Framework for Healthcare Databases Using Python
The arrival of data-driven healthcare systems requires high-quality data sets to enable clinical research and decision-making. However, healthcare databases always contain various data quality issues, including inconsistency, duplication, and logical error, which prevent the analysts to conduct an analysis that has validation. This paper develops a DQACFHD that integrates advanced theories of data quality management with the feasible procedures for implementing the theory. We retrieved a public health care data set from Kaggle and utilized it to implement our DQACFHD tool. The methodologies conducted data profiling, rule-based validation technique, duplicate identification, outlier removal, and logical inconsistency checks using the four quality dimensions. These are dubbed completeness, uniqueness, validity, and accuracy. We utilized Pythons libraries, such as Pandas, NumPy, Matplotlib, to automate the data cleaning process and machine learning model and visualize the data quality post-processing. Our DQACFHD tool removed all the duplicates and inconsistencies in the data, as evidenced by the 100% score on the quality dimensions by applying different strategies in the automated cleaning process. The post-processing analysis further demonstrated a realistic data distribution across demographic and financial dimensions. Therefore, the results show how automation based on Python has made it realistic to attain high data quality, efficiency, and reproducibility for health purposes given the application use. Thus, the application may ensure better data in different health data applications. The tool can also facilitate future work on data governance and analytical frameworks
Encryption Strategies for Secure Big Data Storage: A Study of AWS S3 and Redshift Clusters
As the big data continues to grow exponentially, safeguarding the data and having the tighten access control mechanisms has become a problem that enterprises and researchers are in need of. Scalable and cost effective storage solutions are meted out via cloud based storage solutions such as Amazon Web Services (AWS) Simple Storage Service (S3) and Redshift. However, these services require numerous encryption strategies in place to make the data safe, confidential, secure, and compliant with the industry standards. In this paper we discuss the encryption techniques to be used in securing big data inside the AWS S3 and Redshift clusters. A comparative performance, security effectiveness and computational overhead analysis of server side encryption (SSE), client side encryption (CSE) and column level encryption are made. In addition to this, the study introduces the mathematical models to evaluate encryption performance, along with an optimum encryption framework to provide a balance between security and performance for big data workloads. These findings provide insight into the encryption mechanisms of cloud storage based encryption and give some guidelines how efficient and secure encryption policies can be achieved
When AI Acts: Opportunities and Risks of Agentic Systems
An agentic artificial intelligence (AI) marks a revolutionary new class of system that can perform, on its own, actions that are directed towards achieving some goals. In contrast to traditional reactive AI models, which simply respond to input, agentic systems comprehend their surroundings, reason strategically and can even work independently in a changing environment. Their birth is in line with the development of large language models, reinforcement learning and autonomous orchestration frameworks, which give machines the ability to plan and carry out tasks without the need for constant human intervention. This paper is about the issues connected with the agentic AI that make it so popular at this moment in time, the benefits brought by it and the hazards it causes. The possible uses extend from robotics and cybersecurity to finance and scientific discovery, where the capability of making decisions independently can open up the vast efficient use of time. However, the existence of these systems means the possibility of substantial risks, such as among others, misalignment, emergent behaviors, accountability gaps, and ethical uncertainties. The current article makes a detailed conceptualization framework for the comprehension of agentic AI: it specifies the features of the system, shows its track, considers the implications, and suggests the strategies for management, as well as, by providing the example of AutoGPT, tries to convey both the promise and the danger in the autonomous action of AI
Emergence of AI Trust Layers & Governance
The rapid ascent of artificial intelligence has opened up a plethora of possibilities for various sectors; however, it has equally generated some very severe problems with regard to trust, accountability, and the responsible use of the technology. Consequently, as AI systems progressively become the mediators of the decisions in the fields of medical care, finance, governance, and also in our daily lives, issues with fairness, transparency, bias, and taking advantage of the technology have surfaced. There is the concept of “trust layers”, which has been propounded to reconcile this widening gulf that is between dependability and innovation – these are specialised mechanisms, frameworks, and safeguards that double up as checks and balances, assuring that AI is not only efficient but safe, ethical, and in line with human values. The technical guardrails that track the models in real time, the organisational processes that act as a governor on how AI is deployed, and the societal oversight that weighs innovation against collective well-being are some of the levels these layers function in. Decision-making is the core of these trust layers where governance is situated and which acts as the backbone of the responsible conduct of AI by setting standards, forming accountability structures, and instituting transparent practices that make the trust grow with the use. This article outlines how governance, combined with trust-enabling layers, is a game changer for AI making it a technology of certainty rather than one of doubt. We develop a coherent framework demonstrating the implementability of trust layers all through the AI lifecycle, practically facilitated through governance examples. So as to better understand these concepts, we add a real-life situation that exemplifies how these ideas were implemented in an actual AI case, thereby deriving learnings from both triumphs and predicaments. Collectively, these revelations confirm that trust is not a stumbling block to but rather a stepping stone of innovation, and that the fate of AI is so much dependent on our ways of governing and protecting it as on our ability to construct it